class_docstr: Polynomial interpolation between `initial_p` and `final_p`.
  
  Over `schedule_timesteps`. After this many time steps, always returns
  `final_p`.
docstr: Initializes a PolynomialSchedule instance.
  
  Args:
      schedule_timesteps: Number of time steps for which to
          linearly anneal initial_p to final_p
      final_p: Final output value.
      framework: The framework descriptor string, e.g. "tf",
          "torch", or None.
      initial_p: Initial output value.
      power: The exponent to use (default: quadratic).
docstr_tok: ["Initializes","a","PolynomialSchedule","instance",".","Args",":","schedule_timesteps",":","Number","of","time","steps","for","which","to","linearly","anneal","initial_p","to","final_p","final_p",":","Final","output","value",".","framework",":","The","framework","descriptor","string",",","e.g",".","``","tf","''",",","``","torch","''",",","or","None",".","initial_p",":","Initial","output","value",".","power",":","The","exponent","to","use","(","default",":","quadratic",")","."]
code: def __init__(
      self,
      schedule_timesteps: int,
      final_p: float,
      framework: Optional[str],
      initial_p: float = 1.0,
      power: float = 2.0,
  ):
      """Initializes a PolynomialSchedule instance.
  
      Args:
          schedule_timesteps: Number of time steps for which to
              linearly anneal initial_p to final_p
          final_p: Final output value.
          framework: The framework descriptor string, e.g. "tf",
              "torch", or None.
          initial_p: Initial output value.
          power: The exponent to use (default: quadratic).
      """
      super().__init__(framework=framework)
      assert schedule_timesteps > 0
      self.schedule_timesteps = schedule_timesteps
      self.final_p = final_p
      self.initial_p = initial_p
      self.power = power
  
file_path: ray/rllib/utils/schedules/polynomial_schedule.py
imports: from typing import Optional
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.schedules.schedule import Schedule
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 to create a new PolynomialSchedule object from inherited base classes: Schedule with example: obj = PolynomialSchedule(schedule_timesteps, final_p, framework, initial_p, power)